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Reinforcement Learning for Offboard Control of a Racing Drone
Reinforcement Learning for Offboard Control of a Racing Drone
Autonomous drone racing using offboard control is very challenging because a limited amount of sensor data are available to the offboard computer. Offboard control however, allows using extremely lightweight drones that can reach much higher performances than heavier drones using onboard control. This project has two goals: First, implement and test a communication interface between a ROS-based flight stack and a C/Python-based codebase for low-latency high-bandwidth communication between the drone and an offboard computer. Second, use reinforcement learning to develop a policy that can successfully fly an offboard-controlled racing drone in the real world. Requirements: Strong background in robotics and machine learning is required. ROS, C++, and Python skills. Experience with quadrotor hardware and drone flight is a plus.
Autonomous drone racing using offboard control is very challenging because a limited amount of sensor data are available to the offboard computer. Offboard control however, allows using extremely lightweight drones that can reach much higher performances than heavier drones using onboard control. This project has two goals: First, implement and test a communication interface between a ROS-based flight stack and a C/Python-based codebase for low-latency high-bandwidth communication between the drone and an offboard computer. Second, use reinforcement learning to develop a policy that can successfully fly an offboard-controlled racing drone in the real world. Requirements: Strong background in robotics and machine learning is required. ROS, C++, and Python skills. Experience with quadrotor hardware and drone flight is a plus.
Not specified
Please send your CV and transcripts (bachelor and master) to Christian Pfeiffer (cpfeiffe AT ifi DOT uzh DOT ch), Angel Romero (roagui AT ifi DOT uzh DOT ch), and Yunlong Song (song AT ifi DOT uzh DOT ch).
Please send your CV and transcripts (bachelor and master) to Christian Pfeiffer (cpfeiffe AT ifi DOT uzh DOT ch), Angel Romero (roagui AT ifi DOT uzh DOT ch), and Yunlong Song (song AT ifi DOT uzh DOT ch).